An Advanced Steering System with Active Kinesthetic Feedback for Handling Qualities Improvement

SUMMARY

Advanced Steering System with artificial steering wheel torque-active kinesthetic information feedback for improving handling qualities is discussed. Fundamentally the structure of the system may be considered to another form of model following control. In this system, a driver always remains in the control loop and receives steering control information which give him/her a direct hint to steer a steering wheel. This system works as a stability and control augmentation system of the vehicle to improve the vehicle handling qualities both in compensatory and pursuit control task, and is expected to reduce driver's workload. Effects of this system are analyzed in terms of man-machine system characteristics. Identification of driver dynamics was carried out to find why such improvement could be achieved. Availability of the proposed system is verified by analysis, simulator and proving ground tests.     

Advanced Steering System Adaptable to Lateral Control Task and Driver's Intention

This paper proposes an advanced steering system that adaptively varies the static gain and dynamics of the steering system. The steering system gain is adjusted, depending on whether the driver is in an aggressive or leisurely driving mood. The steering system dynamics is so designed that the command mode of the steering system will be either a rate-command or an attitude-command according to the lateral control task performed by the driver. The recognition system for lateral control tasks, a lane-following or lane-change task is proposed. The findings of simulator tests indicate proposed advanced steering system would remarkably improve the vehicle handling qualities.     

Advanced Steering System Adaptable to Lateral Control Task and Driver's Intention

This paper proposes an advanced steering system that adaptively varies the static gain and dynamics of the steering system. The steering system gain is adjusted, depending on whether the driver is in an aggressive or leisurely driving mood. The steering system dynamics is so designed that the command mode of the steering system will be either a rate-command or an attitude-command according to the lateral control task performed by the driver. The recognition system for lateral control tasks, a lane-following or lane-change task is proposed. The findings of simulator tests indicate proposed advanced steering system would remarkably improve the vehicle handling qualities.     

Objectification of on-center handling characteristics based on spring-mass-damper system

As one of the major handling performance factors of vehicles and tires, on-center handling is very important at high speeds where safety and refinement are major concerns for the driver. In this paper, the steering wheel torque and vehicle response characteristics that play an important role in on-center handling performance were quantified using a spring-mass-damper (SMD) system. Using this system, the characteristics of steering wheel torque and vehicle response could be objectified with SMD parameters such as those for the spring and damping. Experimental objective tests were performed by considering the process by which the on-center handling is evaluated subjectively, and the SMD parameters were extracted from the measurement data. From a statistical analysis of the subjective and SMD parameters for several vehicles and professional drivers, it was found that the subjective assessment of on-center handling could be successfully explained using the suggested parameters.     

线控转向系统路感模糊控制仿真分析

驾驶员希望通过转向盘的力矩信息感知汽车的行驶状态。文章主要研究汽车转向盘力特性与转向盘转角、车速、侧向加速度及转向阻力矩的关系,运用多变量模糊控制技术研究了线控转向系统的路感,通过ADAMS提供离线汽车数据,在Matlab／Simulink中对路感多变量模糊控制器进行了仿真,并对其中一种控制结构进行了硬件在环试验,给出了路感多变量模糊控制的一种参数调整方法以及路感数据。表明仿真同硬件在环仿真结果基本一致,验证了路感多变量模糊控制方法可行。     

A lateral driver model for vehicle–driver closed-loop simulation at the limits of handling

This paper presents a lateral driver model for vehicle–driver closed-loop simulation at the limits of handling. An appropriate driver model can be used to evaluate the performance of vehicle chassis control systems via computer simulations before vehicle tests which incurs expenses especially at the limits of handling. The driver model consists of two parts. The first part is an upper-level controller employing force-based approach to reduce the number of unknown vehicle parameters. The feedforward part of the upper controller has been designed by using the centre of percussion. The feedback part aims to minimise ‘tangential error’, defined as the sum of body slip angle and yaw error, to match vehicle direction and road heading angle. The part is designed to regenerate an appropriate skid motion similar to that of a professional driver at the limits. The second part is a lower-level controller which converts the desired front lateral force to steering wheel angle. The lower-level controller also consists of feedforward and feedback parts. A two-degree-of-freedom bicycle model-based feedforward part provides nominal steering wheel angle, and the feedback part aims to eliminate unmodelled error. The performance of the lateral driver model has been investigated via computer simulations. It has been shown that the steering behaviours of the proposed driver model are quite close to those of a professional driver at the limits. Compared with the previously developed lateral driver models, the proposed lateral driver model shows good tracking performance at the limits of handling.     

Lateral handling improvement with dynamic curvature control for an independent rear wheel drive EV

The integrated longitudinal and lateral dynamic motion control is important for four wheel independent drive (4WID) electric vehicles. Under critical driving conditions, direct yaw moment control (DYC) has been proved as effective for vehicle handling stability and maneuverability by implementing optimized torque distribution of each wheel, especially with independent wheel drive electric vehicles. The intended vehicle path upon driver steering input is heavily depending on the instantaneous vehicle speed, body side slip and yaw rate of a vehicle, which can directly affect the steering effort of driver. In this paper, we propose a dynamic curvature controller (DCC) by applying a the dynamic curvature of the path, derived from vehicle dynamic state variables; yaw rate, side slip angle, and speed of a vehicle. The proposed controller, combined with DYC and wheel longitudinal slip control, is to utilize the dynamic curvature as a target control parameter for a feedback, avoiding estimating the vehicle side-slip angle. The effectiveness of the proposed controller, in view of stability and improved handling, has been validated with numerical simulations and a series of experiments during cornering engaging a disturbance torque driven by two rear independent in-wheel motors of a 4WD micro electric vehicle.     

Effects of Free-Control Variables on Automobile Handling

Compared with the fixed-control case, relatively few studies of the effects on handling quality of the nature of the free-control response of an automobile to steering torque inputs have been reported. Prior to reviewing these studies, an attempt is made in this paper to provide a conceptual framework for assessing the results, by drawing on analytical and experimental work concerned with manual control in closed-loop tracking systems. Application of these ideas to the automobile shows that a fixed-control driver strategy is required where precise path control is necessary. Less demanding situations would allow a free-control driving mode. Steering task performance is found to be relatively insensitive to free-control vehicle responses. However, drivers exhibit clear preferences for certain ranges of steering torque gradient, and for rapid responses of steering wheel angle to torque inputs. Vehicle handling variables interact strongly in their effect on driver opinion. For example, the optimum steering torque gradient (in N m/G) decreases, and the optimum steering “stiffness” (in N m/rad) increases, as the fixed-control response sensitivity increases. Within fairly wide ranges, the damping of the free-control oscillatory mode has little effect on handling quality.     

Effects of Free-Control Variables on Automobile Handling

SUMMARY

Compared with the fixed-control case, relatively few studies of the effects on handling quality of the nature of the free-control response of an automobile to steering torque inputs have been reported. Prior to reviewing these studies, an attempt is made in this paper to provide a conceptual framework for assessing the results, by drawing on analytical and experimental work concerned with manual control in closed-loop tracking systems. Application of these ideas to the automobile shows that a fixed-control driver strategy is required where precise path control is necessary. Less demanding situations would allow a free-control driving mode. Steering task performance is found to be relatively insensitive to free-control vehicle responses. However, drivers exhibit clear preferences for certain ranges of steering torque gradient, and for rapid responses of steering wheel angle to torque inputs. Vehicle handling variables interact strongly in their effect on driver opinion. For example, the optimum steering torque gradient (in N m/G) decreases, and the optimum steering “stiffness” (in N m/rad) increases, as the fixed-control response sensitivity increases. Within fairly wide ranges, the damping of the free-control oscillatory mode has little effect on handling quality.     

Steering disturbance rejection using a physics-based neuromusculoskeletal driver model

The aim of this work is to develop a comprehensive yet practical driver model to be used in studying driver–vehicle interactions. Drivers interact with their vehicle and the road through the steering wheel. This interaction forms a closed-loop coupled human–machine system, which influences the driver's steering feel and control performance. A hierarchical approach is proposed here to capture the complexity of the driver's neuromuscular dynamics and the central nervous system in the coordination of the driver's upper extremity activities, especially in the presence of external disturbance. The proposed motor control framework has three layers: the first (or the path planning) plans a desired vehicle trajectory and the required steering angles to perform the desired trajectory; the second (or the musculoskeletal controller) actuates the musculoskeletal arm to rotate the steering wheel accordingly; and the final layer ensures the precision control and disturbance rejection of the motor control units. The physics-based driver model presented here can also provide insights into vehicle control in relaxed and tensed driving conditions, which are simulated by adjusting the driver model parameters such as cognition delay and muscle co-contraction dynamics.     

Steering torque control using variable impedance models for a steer-by-wire system

This paper presents a novel sensor-less steering torque control method for applications to the steer-by-wire system. A steer-by-wire system has not any mechanical link to connect a steering wheel and a rack and pinion gear module. Instead of mechanical devices, two electric motors are used on each side. A one motor is attached to the steering wheel and the other is set on rack and pinion. The motor on the steering wheel works as a deliverer between a steering torque and load torque from the road. In this paper, we focus on motion control related to the steering feel based on impedance control. Therefore, the model of rack and pinion is not considered in this work. In most power steering systems, a torque sensor is used to set impedance effect on driver’s steering feel. In this paper, we proposed a novel steering control method without using any torque sensors. The effectiveness of a proposed method is confirmed from experimental results.     

Automotive steering system preferences evaluated using a driving simulator

The automotive steering system is the primary channel through which road and vehicle behavior feedback is transmitted to the driver. While the driver provides directional platform control through the steering wheel, perceptions of the vehicle’s handling responsiveness are simultaneously transmitted back to the driver allowing for correction of any instabilities the vehicle may encounter. Based on these factors, drivers often pay special attention to the steering system when deciding what vehicle to purchase. Therefore, a significant amount of effort and time is invested in attempting to determine the optimal design of steering system components and configurations. In this study, the determination of an optimal steering configuration was attempted based on responses obtained from questionnaires that subjects answered. The questions were designed to evaluate the degree of satisfaction regarding the “control”, “ease of operation”, and “fun” participants experienced after each driving run. During the study, human subjects drove a driving simulator for 15 combinations of 3 different roadway environments and 5 different steering configurations, filling out a questionnaire after each scenario. The subjects were also classified as a type of driver (“utility”, “enthusiast”, and/or “performance”). The study attempted to determine if the mean values of questionnaire responses for “control”, “ease”, and “fun” type of questions changed as the scenario and/or driver type changed. Analysis of Variance (ANOVA) was used to determine if the mean values of the three types of questions were statistically different. The overall results suggest that the average responses for vehicle “control”, “ease”, and the “fun” type of questions were dependent on the type of roadway environment; however, only the responses for “fun” type of questions were influenced by the given steering configurations. Indeed, the steering system can impact the driver’s perceptions of the vehicle’s operational experience.     

Haptic steering support for driving near the vehicle’s handling limits; skid-pad case

Current vehicle dynamic control systems from simple yaw control to high-end active steering support systems are designed to primarily actuate on the vehicle itself, rather than stimulate the driver to adapt his/her inputs for better vehicle control. The driver though dictates the vehicle’s motion, and centralizing him/her in the control loop is hypothesized to promote safety and driving pleasure. Exploring the above statement, the goal of this study is to develop and evaluate a haptic steering support when driving near the vehicle’s handling limits (Haptic Support Near the Limits; HSNL). The support aims to promote the driver’s perception of the vehicle’s behaviour and handling capacity (the vehicle’s internal model) by providing haptic (torque) cues on the steering wheel. The HSNL has been evaluated in (a) driving simulator tests and (b) tests with a vehicle (Opel Astra G/B) equipped with a variable steering feedback torque system. Drivers attempted to achieve maximum velocity while trying to retain control in a circular skid-pad. In the simulator (a) 25 subjects drove a vehicle model parameterised as the Astra on a dry skid-pad while in (b) 17 subjects drove the real Astra on a wet skid-pad. Both the driving simulator and the real vehicle tests led to the conclusion that the HSNL assisted subjects to drive closer to the designated path while achieving effectively the same speed. With the HSNL the drivers operated the tires in smaller slip angles and hence avoided saturation of the front wheels’ lateral forces and excessive understeer. Finally, the HSNL reduced their mental and physical demand.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

Additional 4WS and Driver Interaction

This investigation is based on a complex 4-wheel vehicle model of a passenger car that includes steering system and drive train. The tyre properties are described for all possible combined longitudinal and lateral slip values and for arbitrary friction conditions. The active part is an additional steering system of all 4 wheels, additionally to the driver's steering wheel angle input. Three control levels are used for the driver model that thereby can follow a given trajectory or avoid an obstacle.

The feedback control of the additional 4 wheel steering is based on an observer which can also have adaptive characteristics. Moreover a virtual vehicle model in a feedforward scheme can provide desired steering characteristics.

To get information for critical situations a cornering manoeuvre with sudden u-split conditions is simulated. Further a similar manoeuvre is used to evaluate the reentry in a high friction area from low friction conditions. And finally the performance of the controller is shown in a severe lane change manoeuvre.     

直线行驶过程中汽车方向盘转角的研究分析

介绍了一种汽车方向盘转角传感器,搭建了汽车方向盘转角数据采集系统。依据在北京五环路上的行驶实验,分析了驾驶前期和后期方向盘转角特性的不同,得出驾驶员正常驾驶汽车直线行驶时方向盘转角范围约在其回正位置的左右7°之间,方向盘紧定时间在9s内。实验结果为驾驶行为研究、驾驶疲劳监测及相关产品的开发提供了参考。     

Nonlinear PI front and rear steering control in four wheel steering vehicles

Vehicle steering dynamics show resonances, which depend on the longitudinal speed, unstable equilibrium points and limited stability regions depending on the constant steering wheel angle, longitudinal speed and car parameters.

The main contribution of this paper is to show that a combined decentralized proportional active front steering control and proportional-integral active rear steering control from the yaw rate tracking error can assign the eigenvalues of the linearised single track steering dynamics, without lateral speed measurements, using a standard single track car model with nonlinear tire characteristics and a non-linear first-order reference model for the yaw rate dynamics driven by the driver steering wheel input. By choosing a suitable nonlinear reference model it is shown that the responses to driver step inputs tend to zero (or reduced) lateral speed for any value of longitudinal speed: in this case the resulting controlled vehicle static gain from driver input to yaw rate differs from the uncontrolled one at higher speed. The closed loop system shows the advantages of both active front and rear steering control: higher controllability, enlarged bandwidth for the yaw rate dynamics, suppressed resonances, new stable cornering manoeuvres, enlarged stability regions, reduced lateral speed and improved manoeuvrability; in addition comfort is improved since the phase lag between lateral acceleration and yaw rate is reduced.

For the designed control law a robustness analysis is presented with respect to system failures, driver step inputs and critical car parameters such as mass, moment of inertia and front and rear cornering stiffness coefficients. Several simulations are carried out on a higher order experimentally validated nonlinear dynamical model to confirm the analysis and to explore the robustness with respect to unmodelled dynamics.     

基于统一模型的转向-悬架系统最优综合控制方法

通过对车辆底盘系统中的转向和悬架系统建立统一的数学模型,利用M atlab/S imu link仿真,结合最优控制理论,分别对被动悬架兼前轮转向系统与主动悬架兼四轮转向综合控制系统进行了对比研究。理论分析与仿真试验表明,综合控制系统下车辆的操纵稳定性和平顺性都得到了很大的提高。     

Additional 4WS and Driver Interaction

SUMMARY

This investigation is based on a complex 4-wheel vehicle model of a passenger car that includes steering system and drive train. The tyre properties are described for all possible combined longitudinal and lateral slip values and for arbitrary friction conditions. The active part is an additional steering system of all 4 wheels, additionally to the driver's steering wheel angle input. Three control levels are used for the driver model that thereby can follow a given trajectory or avoid an obstacle.

The feedback control of the additional 4 wheel steering is based on an observer which can also have adaptive characteristics. Moreover a virtual vehicle model in a feedforward scheme can provide desired steering characteristics.

To get information for critical situations a cornering manoeuvre with sudden u-split conditions is simulated. Further a similar manoeuvre is used to evaluate the reentry in a high friction area from low friction conditions. And finally the performance of the controller is shown in a severe lane change manoeuvre.